Automated Tax Code Assignment Using Community Transaction Data
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Solution Overview
Problem
Current e-procurement systems face inefficiencies due to incorrect or missing tax codes in invoices, leading to wasteful use of computer processing resources and network bandwidth, as administrators repeatedly communicate to gather necessary information for correct tax code assignment.
Innovation Solution
A procurement control system that automates the assignment of tax codes using a multi-tenant database system, where tax codes are determined based on parameters like supplier and buyer countries, commodity type, and deductibility type, utilizing community transaction data to reduce the need for repeated communications and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If administrators manually communicate to gather information for tax code assignment, then tax code accuracy is improved, but computer processing resource consumption and network bandwidth usage increase
Solution Approach 1:
The system enables self-service automated tax code assignment by using machine learning models trained on community transaction data to automatically determine commodity types and assign tax codes without administrator intervention, thereby reducing manual communication while maintaining accuracy
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on historical community transaction data before actual tax code assignment is needed, so that when invoices are processed, the models are already prepared to accurately and efficiently determine commodity types and assign tax codes
2Measurement precision
If administrators manually communicate to gather information for tax code assignment, then tax code accuracy is improved, but network bandwidth consumption increases
Solution Approach 1:
The system enables self-service automated tax code assignment by using machine learning models trained on community transaction data to automatically determine commodity types and assign tax codes without administrator intervention, thereby reducing manual communication while maintaining accuracy
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a mediator between invoice data and tax code assignment, using community transaction data as training material to bridge the gap without requiring direct administrator communication
3Productivity
If automated tax code assignment is implemented without community transaction data, then processing speed is improved, but tax code accuracy deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical community transaction data before actual tax code assignment is needed, so that when invoices are processed, the models are already prepared to accurately and efficiently determine commodity types and assign tax codes
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a mediator between invoice data and tax code assignment, using community transaction data as training material to bridge the gap without requiring direct administrator communication
4Measurement precision
If repeated communications are used to confirm tax and business statuses, then tax code accuracy is improved, but time consumption increases
Solution Approach 1:
The system enables self-service automated tax code assignment by using machine learning models trained on community transaction data to automatically determine commodity types and assign tax codes without administrator intervention, thereby reducing manual communication while maintaining accuracy
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on historical community transaction data before actual tax code assignment is needed, so that when invoices are processed, the models are already prepared to accurately and efficiently determine commodity types and assign tax codes
Data Source
AI summary
A computer-implemented method comprising receiving a set of rules that define assigning tax codes for a first entity based on a plurality of parameters; receiving invoice data that defines an invoice directed to the first entity; automatically determining a commodity type applicable to the invoice data by digitally cross-referencing line items in the invoice data representing goods or services to community transaction data, the community transaction data comprising a plurality of different line item data for different invoices of other entities different from and unrelated to the first entity; automatically assigning tax codes to the invoice data based on the commodity type and the set of rules; and causing to display the commodity type and the tax codes in a graphical user interface.